A number of additional general issues are relevant in providing predictions of the potential future impacts of environmental change (Botkin et al., 2007). The issue of scale is highly relevant, especially in relation to the particular factors that determine impacts (Botkin et al., 2007;
Vogiatzakis, 2003; Pearson & Dawson 2003; Guisan & Zimmerman 2000; Woodward 1987). A significant proportion of previous research, on the potential ecological impacts of environmental change, has focused on investigating the potential ecological impacts of climate change (Preston et al, 2011; Berry, 2008). The subsequent discussion focuses on the issues apparent within these studies, particularly in modelling distributions. However, the same issues and considerations are also highly relevant to the study of the potential ecological impacts from other factors, and for other predictive modelling approaches.
2.4.1 Spatial Scale
Previous research into the ecological impacts of climate (and other environmental) change has tended to focus on providing assessments for a limited number of species at larger (regional or global) spatial scales or for more complex species assemblages for specific regions or ecosystems (Trivedi et al., 2008; Holman et al., 2005a; Pearson & Dawson, 2003; Guisan & Zimmerman 2000;
Peng 2000; Zimmerman & Kienast., 1999; Brzeziecki et al., 1995; Brzeziecki et al., 1993). Recently,
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it has become increasingly appreciated that investigations of the potential ecological effects of climate change for entire landscapes at sub-regional scales should be a priority for future research (Morecroft et al., 2012; Berry, 2008; Trivedi et al., 2008; Holman et al., 2005a; CBD, 2003; Burnett et al., 1998). However, providing such assessments at this scale presents particular challenges;
partly because of the relative importance of other non-climatic factors in influencing the occurrence, functioning and structure of ecological phenomena and the concomitant problems of reliably incorporating their influence into subsequent evaluations (Pearson & Dawson, 2003;
Guisan & Zimmerman, 2000). Pearson & Dawson (2003) propose a general hierarchy of factors that they regard as playing a dominant role in determining species distribution across various spatial scales (Table 2.1).
Table 2.1: Hierarchy of the dominant distribution controls at different spatial scales (Source: Pearson & Dawson, 2003)
Scale Domain
The hierarchy clearly suggests that climate has influence over the occurrence of species at larger spatial extents, whilst at smaller scales non-climatic factors play a more important role. The table suggests that topographic and land use factors are the most important controls over the dist i utio of spe ies at the la ds ape s ale. The esults of some research support the scale dependencies outlined in the hierarchy. For instance, Pearson et al. (2002) found a good agreement between observed and simulated distributions for 32 plant species based on correlations between observed distributions and five climatic parameters at the European scale.
Beerling et al. (1995) applied a static correlative model driven by three bioclimatic variables to simulate the distribution of Japanese knotweed (Fallopia japonica) for Europe and South-east Asia. The o lude that the Eu opea dist i utio is li ati all dete i ed a d epo t
ge e all a u ate si ulatio s fo South-east Asia.
It should be noted, however, that the assumption that large spatial extents are associated with coarse data resolutions and small extents with fine data resolutions is inherent within the hierarchy. This distinction is important, as it does much to explain the proposed framework and a ts to highlight the fa t that o e s s ale of pe eptio is a iti al o side atio he investigating the ecological effects of climate change. The implication then is not that climate plays no role in influencing species distributions, and other ecological impacts, at sub-regional
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scales but rather that other (non-climatic) factors play a more dominant role at the resolutions that are typically employed for investigation at these scales. The role of climate in influencing ecological impacts at different spatial scales therefore may not be as clear cut as the hierarchy initially suggests.
Pearson & Dawson (2003, pp. 369) themselves state that their hierarchical framework may be imperfect and oversimplified. Woodward a gues that at all spatial s ales the espo se of the pla t to li ate is a u ial featu e i its p ese e . I deed, so e su -regional scale research suggests a significant relationship between climatic variables and the distribution of the ecological units under investigation (e.g. Trivedi et al., 2008). Zimmerman & Kienast (1999) used a static equilibrium model to study the influence of climatic factors on the spatial patterns of graminoid species and communities at the sub-regional scale within Switzerland using fine resolution (50m) data. The o luded that the li ati fa to s used to d i e the odel e plai ed a ajo pa t of the observed patte s )i e a & Kie ast, 1999, pp. 469).
It is reasonable to suppose therefore that climate does play some role in shaping distribution within a sub-regional context: particularly in relation to spatial extents which are defined as la ds ape u der Pearson & Da so s (2003) nomenclature. As such, the use of climatic parameters as predictor variables to model potential changes in distribution may well provide a useful indication of the potential ecological impact of climate change within a sub-regional context. It should also be noted that Pearson & Da so , pp. state that ide tif i g appropriate scales of analysis for different environmental drivers, thus validating [or not] the scale dependencies outlined in Fig. 5 [their proposed hierarchical framework], should be the focus of fu the esea h . Ce tai l , the e te t to hi h li ate di e tl i flue es the dist i utio of ecological phenomena at such scales is a valid area of further investigation from an academic and conservation management perspective, particularly when considering the threat that current and future climate change poses to the integrity of existing ecological systems (IPCC, 2007a; 2002;
MA, 2005a; 2005b; CBD, 2003).
2.4.2 Ecological Scale
Scale is also important in influencing the appropriate level of ecological organisation that should be the focus of research investigating the potential ecological impacts of climate change (as well as other relevant factors). Although, some studies have focused at the level of community organisation (e.g. Zimmerman & Kienast, 1999; Brzeziecki et al., 1995; Brzeziecki et al., 1993), previous research has tended to focus on species as the basic ecological units of investigation (Berry et al., 2006; 2002; Vogiatzakis 2003; Pearson et al., 2002; Guisan & Zimmerman, 2000; Wu
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& Smeins, 2000; Franklin, 1998; Tremblay-Boyer & Anderson, 2007). From a theoretical perspective, the general focus on modelling at the species, rather than community, level within climate change related ecological research is conceptually sound. It is embedded in the discourse surrounding the community/continuum debate and is therefore intrinsically linked to the concept of the niche (Guisan & Zimmerman, 2000; Franklin, 1995). In relation to predictive change modelling, the community theory essentially implies that a community acts as a cohesive ecological unit and can therefore be treated in the same way as other more readily identifiable units (i.e. species). The continuum postulate regards the community as a far less cohesive entity, as it emphasises the individualistic response of species within the community to environmental gradients (continua). The theory suggests that extant communities are unlikely to move as a cohesive unit under conditions of future change (Guisan & Zimmerman, 2000; Zimmerman &
Kienast, 1999; Franklin, 1995; Begon et al., 1990; Pears, 1985). Generally, the current perspective within ecology is closer to the individualistic, continuum concept. It is argued, therefore, that models simulating species rather than community distributions are more robust (Guisan &
Zimmerman 2000; Zimmerman & Kienast, 1999; Begon et al., 1990).
From a general conservation policy perspective however, it is desirable to focus investigation at levels of ecological organisation higher than that of the species (Morecroft et al., 2012;
Zimmerman & Kienast 1999; Burnett et al., 1998; Franklin, 1995; Tremblay-Boyer & Anderson, 2007). For instance, Morecroft et al. (2012, pp. 549) suggest that assessments of ecological vulnerability at the community level represent a more effective approach, because vulnerability at this scale of organisation encompasses and is enhanced the ul e a ilit a d apa it fo ha ge at ge ot pe a d spe ies le els . Si ila l , assess e ts at the la ds ape s ale are likely to be more useful than those at smaller spatial scales (e.g. local), as they will take account of a broader network of sites and so better represent vulnerability at higher levels of ecological organisation (Morecroft et al., 2012).
Furthermore, Zimmerman & Kienast (1999, pp 470) state that ...election of either the species or community approach depends heavily on the aim of the study. The focus on communities is related to the emphasis on concrete landscape patterns.... This quote carries the implicit suggestion that communities should be the emphasis of investigations at the sub-regional, la ds ape scale in order to provide a more holistic assessment. It is worth noting that their research found a higher degree of coincidence between simulated and observed patterns for communities than for species. Franklin (1995, pp. 483) asserts that, although fe e defi itio al u e tai ties o a st a tio s a e asso iated ith the p edi ti e appi g of spe ies dist i utio s,
o u ities a e geog aphi e tities a d the efo e a e p edi ati el apped .
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There is also some suggestion by Zimmerman & Kienast (1999) and others (e.g. Vogiatzakis, 2003) that the use of species for investigating the potential ecological consequences of changes in climate is conceptually problematic within a static-empirical modelling context. This is because the observed presence of a species (on which the modelling is partially based) is by definition an expression of its realised niche (Pearson & Dawson, 2003; Guisan & Zimmerman 2000). It is therefore context sensitive and varies according to the influence of other species present (Pearson & Dawson, 2003). Climate change is likely to have an uncertain, chaotic and largely unpredictable influence on the interactions and interrelationship between these species (Pearson
& Dawson, 2003). This implies that the reliability of results from assessments adopting a species- based focus, within particular modelling contexts (i.e. static empirical), is questionable.
Additionally, as suggested in the discussion below, it is extremely difficult to provide robust, holistic community level simulations at intermediate to large spatial scales by using species as the basic units of investigation (Guisan & Zimmerman, 2000).
2.4.3 Modelling Approaches
The inherent interconnectedness and complexity of many ecological systems and the limitations in the extent of human knowledge and understanding of them is an important area of uncertainty, and presents a serious challenge in understanding the potential ecological effects of climate (and other environmental) change (Pearson & Dawson, 2003; Guisan & Zimmerman, 2000). Various methods have been used to investigate these effects. Such studies commonly attempt to explore climatically-induced ecological change by characterising the environmental requirements (niche) of species or communities and then using this to model potential changes in their distribution or geographical shifts in their suitable climate space under scenarios of future climate change (Guisan & Thuiller, 2005; Vogiatzakis 2003; Pearson & Dawson 2003; Guisan &
Zimmerman 2000). Some general limitations, stemming from the different theoretical assumptions, practical considerations and related methodological practices associated with each of these various approaches, can be identified. The diversity of methodologies and techniques that have been used in an attempt to investigate the distribution of ecological phenomena makes concise classification difficult (Guisan & Zimmerman, 2000). However, a general distinction can be ade et ee those app oa hes that a e stati o d a i Pea so et al., 2002; Beerling et al., 1995).
Stati odels tend to base their predictions on the statistical analysis of large-scale field data sets (Botkin et al., 2007; Guisan & Zimmerman, 2000; Zimmerman & Kienast, 1999). Specifically, such models attempt to characterise the environmental requirements or tolerances (i.e. niche) of
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the ecological units under investigation (e.g. species, communities) by establishing a correlation between their current distributions and environmental factors deemed relevant to their survival (Pearson & Dawson, 2003; Guisan & Zimmerman, 2000). Environmental conditions outside of the current range of the target ecological units are regarded as outside of their environmental niche and therefore unsuitable for their presence or survival. This environmental niche is often referred to as the spe ies o o u ities e i o e tal e elope , or where the niche is established solel i te s of li ati fa to s; io li ati e elope Pea so & Da so , . The environmental or bioclimatic envelope information is then applied to spatial data depicting the future characteristics of relevant environmental conditions or resources in order to predict potential ecological impacts.
Such models are therefore correlative and empirical in nature. Their classification as static relates to the assumption that the observed relationships between the ecological units (e.g. species) and the various environmental (typically climatic) controls under investigation will continue to be maintained in the future (Pearson & Dawson 2003; Guisan & Zimmerman 2000). This assumption is often automatically incorporated due to the inherent characteristics of the relatively simple, statistical models that tend to be used (Guisan & Zimmerman 2000). A significant criticism of the static-empirical approach is that the derived results potentially misrepresent the potential future distribution or suitable climate space of the ecological units under investigation. This is because the sources of data and methods of analysis typically used often only facilitate characterisation of the spe ies u e tl ealised i he a d o l allo fo su se ue t p edi tio s to e ased around the assumption that this niche will continue to hold under conditions of future change (Guisan & Thuiller 2005; Pearson & Dawson 2003; Vogiatzakis 2003; Guisan & Zimmerman 2000).
It is argued that this assumption is problematic, as it is likely that the realised niche that a given species will occupy in the future will be different due to the dynamic and individualistic response of different species to climate change (Thuiller et al., 2005; Pearson & Dawson, 2003).
Although d a i odels are also essentially concerned with identifying the environmental or bioclimatic envelopes of the ecological units under investigation (Pearson & Dawson, 2003) they aim to represent the dynamic physiological interactions and responses of ecological units to their environment more explicitly (Guisan & Zimmerman 2000). In many ways, the a ge of d a i models represents an attempt to address some of the problems generally associated with the static approach. It is argued that dynamic models are likely to produce more reliable predictions under climate change conditions for two interrelated reasons. First, it is suggested that the sources of data used for model parameterisation allow for better characterisation of the fundamental niche (Guisan & Zimmerman 2000). Second, their mechanistic qualities mean that such models are able to offer more realistic simulations by explicitly modelling the way in which
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this fundamental niche will be restricted in the future, due to the influence of dynamic and stochastic factors (e.g. biotic interactions) (Pearson & Dawson 2003; Guisan & Zimmerman 2000).
The recent development of dynamic simulation models undoubtedly represents significant progress towards a more realistic understanding of the potential ecological impact of future changes in climate. However, the complexity of many ecosystems and therefore that of the models required to realistically simulate them is significant (Pearson & Dawson 2003; Guisan &
Zimmerman, 2000). Also, it is generally appreciated that o l e fe spe ies ha e ee studied i detail i te s of thei d a i espo ses to e i o e tal ha ge Guisa & Zimmerman, 2000, pp 148). These issues mean that, in many cases, the use of dynamic models to predict the ecosystem level effects of climate change at larger spatial scales and in a spatially explicit way remains a significant challenge (Botkin et al., 2007; Pearson & Dawson 2003; Guisan &
Zimmerman 2000; Zimmerman & Kienast 1999). Furthermore, the accuracy of results obtained from such models has also been called into question, despite their apparent superiority. This is because they often fail to consider how important non-climatic factors will be influenced under conditions of future climate change. For example, Pearson & Dawson (2003) point out that most modelling effort fails to take any account of the possible role that evolutionary adaptation to climate change may play in influencing the future distribution of the species under investigation.
Ibanez et al. (2006) suggest that elevated atmospheric CO2 is likely to have a significant modifying effect on the interaction of coexisting species and therefore their potential future distribution.
However, the consideration of these effects is often neglected (Pearson et al., 2002). Such issues are apparent within both static and dynamic contexts. However, they demonstrate that the inherent uncertainties associated with the future role of climate change in influencing complex ecological systems means that dynamic approaches are also likely to produce unreliable results. It may be argued that the adoption of such an approach, at present, offers no better guarantee of predictive success.
The limitations associated with both static and dynamic approaches are part of the reason that Pearson & Da so , pp. state that a u ate p edi tio s of iogeog aphi al espo ses to futu e li ate a e ot u e tl possi le . Ho e e , despite the appa e t li itatio s of the static approach and its associated methods, Pearson & Dawson (2003, pp. 361) also suggest that it a p o ide a useful fi st app o i atio as to the pote tiall d a ati i pa t of li ate ha ge o iodi e sit . I deed, e ause highl detailed k o ledge of the ph siolog a d eha iou of the ecological units under investigation is not required (Zimmerman & Kienast, 1999), the static approach potentially offers a more efficient, accessible and appropriate method for investigating the potential impact on ecosystems from climate change in some contexts. For instance, Zimmerman & Kienast (1999) state that their decision to utilise a static equilibrium approach was
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strongly necessitated by the large (high resolution, 50m) data sets required to account realistically for the high vegetation heterogeneity apparent within their study area. Such heterogeneity is a feature of many semi-natural landscapes at the sub-regional scale. This suggests that a static approach represents a potentially more useful methodology for assessing the possible ecological effects of climate change occurring at these scales.